報 告 人：武婷婷 教授

報告題目：Blind Image Deconvolution: New Approaches

報告時間：2024年12月20日（周五）下午3:00

報告地點：靜遠樓204學術報告廳

主辦單位：數(shù)學與統(tǒng)計學院、數(shù)學研究院、科學技術研究院 

報告人簡介：

      武婷婷教授，博士生導師，曾獲得江蘇高?！扒嗨{工程”優(yōu)秀青年骨干教師、江蘇省“科技副總”、南京郵電大學1311人才計劃“鼎新學者”等稱號。目前是中國工業(yè)與應用數(shù)學學會會員、江蘇省運籌學會會員、中國數(shù)學會會員，擔任JMIV, JIMO等雜志及會議審稿人。主要從事優(yōu)化算法及其在信息科學、統(tǒng)計等領域中的應用研究，目前發(fā)表國內(nèi)外知名期刊論文65篇，研究成果發(fā)表于《SIAM Journal on Imaging Sciences》、《Journal of Scientific Computing》以及《IEEE Transactions on Neural Networks and Learning Systems》等國際權威期刊上。

報告摘要：

      Blind image deblurring is challenging task in image restoration. In deblurring, accurate blur kernel estimation in the first stage is crucial, but errors often lead to artifacts. A robust model is proposed that decomposes the blur kernel into deterministic and random components, improving estimation through iterative refinement and utilizing the Lp-norm for better restoration. In addition, a fractional-order variational model is introduced to reduce ringing artifacts, while the Patch-wise Minimal Pixels prior distinguishes between clean and blurred image blocks. These approaches improves restoration quality and has proven convergence, demonstrating superior performance in blind image deblurring.